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Build Luxury E-Commerce Counterfeit Triage via Mastra AI and Oracle OCI

High-value fashion platforms like Pernia's Pop-Up Shop require strict verification to prevent counterfeit listings and return fraud. Build an automated multi-step verification pipeline using Mastra AI framework and Oracle OCI Generative AI inference to flag high-risk transactions. Achieve a 94% precision score on counterfeit item detection.

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What you are building

Construct a high-throughput transaction and item authentication engine using Mastra AI workflows hosted on Oracle OCI Generative AI infrastructure.

How work is evaluated

Evaluation

Evaluates item authenticity classification precision against expert luxury appraiser tags.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

fraud_action_check

Ensures high-risk items trigger immediate hold status

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

detection_precision

Precision of flagged counterfeit listings • target: 0.94 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • Build stateful workflows using TypeScript with Mastra AI agent utilities

  • Integrate Oracle OCI Generative AI service endpoints for scalable model inference

  • Process high-resolution luxury product metadata and customer purchase patterns

  • Enforce step-by-step risk evaluation schemas for automated merchant hold decisions

Resources

Dataset notes

Luxury fashion listing metadata, pricing records, and merchant profile vectors.

Challenge setup

Evaluates item authenticity classification precision against expert luxury appraiser tags.

Challenge input

JSON object with luxury item metadata and seller history

Mastra AI

Provides stateful agent step execution and TypeScript graph orchestrations.

Oracle OCI Generative AI

Provides enterprise cloud runtime for LLM inference calls.

Evaluated output

JSON object with risk score and action flag

Evaluation checks
  • Ensures high-risk items trigger immediate hold status
  • Precision of flagged counterfeit listings • target: 0.94 • range: 0-1
Configured evaluation evidence
  • Benchmark: E-Commerce Fraud Detection Benchmark
  • Detection Precision target: 0.94
  • 1 public reference case
Configured runtime
  • JavaScript execution harness
  • javascript sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Mastra AI · Required
  • Dora · Optional
  • Arcee AI · Optional
Orchestration
  • Mastra AI · Required

Evaluation contract

  • fraud_action_check · Weight 1
  • detection_precision · Weight 1

Metrics

  • detection_precision · Target 0.94

Benchmarks

  • E-Commerce Fraud Detection Benchmark
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with luxury item metadata and seller history

    Output: JSON object with risk score and action flag

    Execution harness: JavaScript

Evaluation tools

  • Mastra AI · Workflow Engine
  • Oracle OCI Generative AI · Inference Infrastructure

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Build Luxury E-Commerce Counterfeit Triage via Mastra AI and Oracle OCI